New Pie and More Pie—Funding for Canada Council for the Arts: An Interview with Guylaine Normandin and Geneviève Vallerand
Bibliographic record
Abstract
The interview took place on 5 February 2016 between when the Canada Council for the Arts had announced major changes to the funding model and when the new funding model would be in effect. It also took place less than 6 months after a federal election, during which the Liberal Party platform included a promise to double funding for Canada Council for the Arts. The interview intended to unpack details, motivations, and challenges behind the new funding model. As well, the interview discussed poor levels of government funding for the arts, inadequate equity, and diversity in Canadian theatre, as well as the struggle many artists have with poverty. The interviewer was Darrah Teitel (Playwright and Grant Recipient) and the subject for the interview was Guylaine Normandin (Director of the Theatre Program for Canada Council for the Arts). Genevieve Vallerand (Head of Media Relations for Canada Council for the Arts) was also present for the conversation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.039 | 0.011 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".